Quality control and preconditioning of seismic data

ABSTRACT

Various implementations directed to quality control and preconditioning of seismic data are provided. In one implementation, a method may include receiving particle motion data from particle motion sensors disposed on seismic streamers. The method may also include performing quality control (QC) processing on the particle motion data. The method may further include performing preconditioning processing on the QC-processed particle motion data. The method may additionally include attenuating noise in the preconditioning-processed particle motion data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 15/117,220, filed Aug. 8, 2016, and titled QUALITY CONTROL ANDPRECONDITIONING OF SEISMIC DATA, which is a 371 of Patent ApplicationSerial Number PCT/US2015/015231 filed Feb. 10, 2015, and titled QUALITYCONTROL AND PRECONDITIONING OF SEISMIC DATA, which claims the benefit ofU.S. provisional patent application Ser. No. 61/937,770, filed Feb. 10,2014 and titled MULTICOMPONENT DATA PRECONDITIONING METHOD ANDAPPARATUS, the entire disclosure of which are herein incorporated byreference.

BACKGROUND

In a seismic survey, a plurality of seismic sources, such as explosives,vibrators, air guns, and/or the like, may be sequentially activated nearthe surface of the earth or in a wellbore to generate energy (i.e.,seismic waves) which may propagate into and through the earth. Theseismic waves may be reflected back by geological formations within theearth, and the resultant seismic wavefield may be sampled by a pluralityof seismic receivers, such as geophones, hydrophones, and/or the like.Each receiver may be configured to acquire seismic data at thereceiver's location, normally in the form of a seismogram representingthe value of some characteristic of the seismic wavefield against time.The acquired seismograms or seismic data may be transmitted wirelesslyor over electrical or optical cables to a recorder system. The recordersystem may then store, analyze, and/or transmit the seismic data. Thisdata may be used to generate an image of subsurface formations in theearth and may also be used to detect the possible presence ofhydrocarbons, changes in the subsurface formations and the like.

Some surveys may be known as “marine” surveys, because they areconducted in marine environments. However, in some scenarios, “marine”surveys may be conducted not only in saltwater environments, but also infresh and brackish waters. In one type of marine survey, called a“towed-array” survey, an array of seismic sensor-containing streamersand sources may be towed behind a survey vessel.

SUMMARY

Various implementations directed to quality control and preconditioningof seismic data are provided. In one implementation, a method mayinclude receiving particle motion data from particle motion sensorsdisposed on seismic streamers. The method may also include performingquality control (QC) processing on the particle motion data. The methodmay further include performing preconditioning processing on theQC-processed particle motion data. The method may additionally includeattenuating noise in the preconditioning-processed particle motion data.

In another implementation, a method may include receiving pressure datafrom pressure sensors disposed on seismic streamers. The method mayfurther include performing quality control (QC) processing on thepressure data. The method may also include performing preconditioningprocessing on the QC-processed pressure data. The method mayadditionally include attenuating noise in the preconditioning-processedpressure data.

In yet another implementation, a method may include receiving particlemotion data from particle motion sensors and pressure data from pressuresensors disposed on seismic streamers. The method may also includeperforming a first quality control (QC) processing on the particlemotion data and a second QC processing on the pressure data. The methodmay further include performing a first preconditioning processing on theQC-processed particle motion data and a second preconditioningprocessing on the QC-processed pressure data. The method mayadditionally include attenuating noise in the preconditioning-processedparticle motion data, and attenuating noise in thepreconditioning-processed pressure data.

The above referenced summary section is provided to introduce aselection of concepts in a simplified form that are further describedbelow in the detailed description section. The summary is not intendedto be used to limit the scope of the claimed subject matter.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted in any part of thisdisclosure. Indeed, the systems, methods, processing procedures,techniques, and workflows disclosed herein may complement or replaceconventional methods for identifying, isolating, and/or processingvarious aspects of seismic signals or other data that is collected froma subsurface region or other multi-dimensional space, includingtime-lapse seismic data collected in a plurality of surveys.

BRIEF DESCRIPTION OF THE DRAWINGS

Implementations of various techniques will hereafter be described withreference to the accompanying drawings. It should be understood,however, that the accompanying drawings illustrate the variousimplementations described herein and are not meant to limit the scope ofvarious techniques described herein.

FIG. 1 illustrates a schematic diagram of a marine-based seismicacquisition system in accordance with implementations of varioustechniques described herein.

FIG. 2 illustrates a flow diagram of a method for performing a qualitycontrol (QC) process on particle motion data in accordance withimplementations of various techniques described herein.

FIG. 3 illustrates a flow diagram of a method for performing apreconditioning process on QC-processed particle motion data inaccordance with implementations of various techniques described herein.

FIG. 4 illustrates a flow diagram of a method for performing a QCprocess on pressure data in accordance with implementations of varioustechniques described herein.

FIG. 5 illustrates a flow diagram of a method for performing apreconditioning process on QC-processed pressure data in accordance withimplementations of various techniques described herein.

FIG. 6 illustrates a schematic diagram of a computing system in whichthe various technologies described herein may be incorporated andpracticed.

DETAILED DESCRIPTION

The discussion below is directed to certain specific implementations. Itis to be understood that the discussion below is for the purpose ofenabling a person with ordinary skill in the art to make and use anysubject matter defined now or later by the patent “claims” found in anyissued patent herein.

It is specifically intended that the claims not be limited to theimplementations and illustrations contained herein, but include modifiedforms of those implementations including portions of the implementationsand combinations of elements of different implementations as come withinthe scope of the following claims.

Reference will now be made in detail to various implementations,examples of which are illustrated in the accompanying drawings andfigures. In the following detailed description, numerous specificdetails are set forth in order to provide a thorough understanding ofthe present disclosure. However, it will be apparent to one of ordinaryskill in the art that the present disclosure may be practiced withoutthese specific details. In other instances, well-known methods,procedures, components, circuits and networks have not been described indetail so as not to obscure aspects of the embodiments.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are used to distinguish oneelement from another. For example, a first object could be termed asecond object, and, similarly, a second object could be termed a firstobject, without departing from the scope of the claims. The first objectand the second object are both objects, respectively, but they are notto be considered the same object.

The terminology used in the description of the present disclosure hereinis for the purpose of describing particular implementations and is notintended to be limiting of the present disclosure. As used in thedescription of the present disclosure and the appended claims, thesingular forms “a,” “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willalso be understood that the term “and/or” as used herein refers to andencompasses one or more possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes” and/or “including,” when used in this specification, specifythe presence of stated features, integers, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, operations, elements, components and/or groupsthereof.

As used herein, the terms “up” and “down”; “upper” and “lower”;“upwardly” and downwardly”; “below” and “above”; and other similar termsindicating relative positions above or below a given point or elementmay be used in connection with some implementations of varioustechnologies described herein. However, when applied to equipment andmethods for use in wells that are deviated or horizontal, or whenapplied to equipment and methods that when arranged in a well are in adeviated or horizontal orientation, such terms may refer to a left toright, right to left, or other relationships as appropriate.

It should also be noted that in the development of any such actualimplementation, numerous decisions specific to circumstance may be madeto achieve the developer's specific goals, such as compliance withsystem-related and business-related constraints, which will vary fromone implementation to another. Moreover, it will be appreciated thatsuch a development effort might be complex and time-consuming but wouldnevertheless be a routine undertaking for those of ordinary skill in theart having the benefit of this disclosure.

The terminology and phraseology used herein is solely used fordescriptive purposes and should not be construed as limiting in scope.Language such as “having,” “containing,” or “involving,” and variationsthereof, is intended to be broad and encompass the subject matter listedthereafter, equivalents, and additional subject matter not recited.

Furthermore, the description and examples are presented solely for thepurpose of illustrating the different embodiments, and should not beconstrued as a limitation to the scope and applicability. While anycomposition or structure may be described herein as having certainmaterials, it should be understood that the composition could optionallyinclude two or more different materials. In addition, the composition orstructure may also include some components other than the ones alreadycited. It should also be understood that throughout this specification,when a range is described as being useful, or suitable, or the like, itis intended that any value within the range, including the end points,is to be considered as having been stated. Furthermore, respectivenumerical values should be read once as modified by the term “about”(unless already expressly so modified) and then read again as not to beso modified unless otherwise stated in context. For example, “a range offrom 1 to 10” is to be read as indicating a respective possible numberalong the continuum between about 1 and about 10. In other words, when acertain range is expressed, even if a few specific data points areexplicitly identified or referred to within the range, or even when nodata points are referred to within the range, it is to be understoodthat the inventors appreciate and understand that any data points withinthe range are to be considered to have been specified, and that theinventors have possession of the entire range and points within therange.

As used herein, the term “if may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context. Similarly, the phrase “if it is determined” or “if [astated condition or event] is detected” may be construed to mean “upondetermining” or “in response to determining” or “upon detecting [thestated condition or event]” or “in response to detecting [the statedcondition or event],” depending on the context.

One or more implementations of various techniques for quality controland preconditioning of seismic data will now be described in more detailwith reference to FIGS. 1-6 in the following paragraphs.

Seismic Data Acquisition

FIG. 1 illustrates a schematic diagram of a marine-based seismicacquisition system 10 in accordance with implementations of varioustechniques described herein. In system 10, survey vessel 20 tows aplurality of seismic streamers 30 (one streamer 30 being depicted inFIG. 1 ) behind the vessel 20. In one implementation, streamers 30 maybe arranged in a spread in which multiple streamers 30 are towed inapproximately the same plane at the same depth. Although varioustechniques are described herein with reference to a marine-based seismicacquisition system shown in FIG. 1 , it should be understood that othermarine-based seismic acquisition system configurations may also be used.For instance, the streamers may be towed at multiple planes and/ormultiple depths, such as in an over/under configuration. In oneimplementation, the streamers may be towed in a slanted configuration,where fronts of the streamers are towed shallower than tail ends of thestreamers.

Seismic streamers 30 may be several thousand meters long and may containvarious support cables, as well as wiring and/or circuitry that may beused to facilitate communication along the streamers 30. Each streamer30 may also be composed of multiple sections connected to one another.In addition, each streamer 30 may have a solid core, liquid core, gelcore, or any other implementation known to those skilled in the art.

Each streamer 30 may include a primary cable where seismic sensor units58 that record seismic signals may be mounted. The seismic sensor units58 may be connected along the streamer 30 at various spacing. Forexample, the seismic sensor units 58 may be spaced in an interleavedmanner along an inline direction of a streamer 30.

Further, the seismic sensor units 58 may be arranged along an outerportion of the core and in a radial direction away from a central axis(not pictured) of the streamer 30. In another implementation, thesensors of each seismic sensor units 58 (e.g., pressure sensors,particle motion sensors, as further described below) may be positionedrelative to one another such that they form an angle of ninety degreeswith respect to the central axis.

In one implementation, each seismic sensor unit 58 may include one ormore pressure sensors capable of detecting a pressure wavefield (i.e.,acquiring pressure data). Such pressure sensors may include one or morehydrophones and/or any other implementation known to those skilled inthe art. For example, each seismic sensor unit 58 may include a singlehydrophone to acquire pressure data. In another example, each seismicsensor unit 58 may include two hydrophones to acquire pressure data. Inanother implementation, the pressure sensors may be spaced apart fromone another such that the pressure sensors may detect particleacceleration by way of measuring a travel time and direction of apressure wave.

In another implementation, each seismic sensor unit 58 may include oneor more particle motion sensors capable of detecting at least onecomponent of a particle motion that is associated with acoustic signalsproximate to the sensor unit 58 (i.e. acquiring particle motion data).Examples of particle motions may include one or more components of aparticle displacement, one or more components (i.e., inline (x),crossline (y) and vertical (z) components (see axes 59)) of a particlevelocity, and one or more components of a particle acceleration.

In particular, a seismic sensor unit 58 may include at least oneparticle motion sensor for purposes of measuring a component of particlemotion along a particular sensitive axis 59 (e.g., the x, y, or z axis).For example, the seismic sensor unit 58 may include a particle motionsensor that is oriented to acquire a measurement of a particle velocityalong the vertical, or z, axis; a particle motion sensor to sense aparticle velocity along the crossline, or y, axis; a particle motionsensor to sense a velocity along the inline, or x, axis; or combinationsthereof, such as multiple particle motion sensors to sense particlevelocities along the three (x, y, and z) axes. In anotherimplementation, the particle motion sensor may be an accelerometerconfigured to measure one or more components of a particle acceleration.In a further implementation, the particle motion sensor may be amicroelectromechanical systems (MEMS) accelerometer. For example, theparticle motion sensor may be three-component MEMS accelerometersconfigured to acquire a measurement of a particle acceleration along thevertical (z-axis), crossline (y-axis), and inline (x-axis) directions.In another example, the particle motion sensor may be two-component MEMSaccelerometers configured to acquire a measurement of a particleacceleration along the vertical (z-axis) and crossline (y-axis)directions.

In yet another implementation, the seismic sensor units 58 may be in theform of multi-component sensors, such that each seismic sensor unit 58is capable of detecting both a pressure wavefield and at least onecomponent of a particle motion that is associated with acoustic signalsthat are proximate to the sensor, such as the components describedabove. In such an implementation, each seismic sensor unit 58 mayinclude both one or more pressure sensors and one or more particlemotion sensors, such as those described above. In particular, dependingon the particular survey, the multi-component seismic sensor unit 58 mayinclude one or more hydrophones, geophones, particle displacementsensors, particle velocity sensors, accelerometers, pressure gradientsensors, or combinations thereof. In one implementation, themulti-component seismic sensor unit 58 may be implemented as a singledevice, as depicted in FIG. 1 , or may be implemented as a plurality ofdevices.

Marine-based seismic data acquisition system 10 may also include one ormore seismic sources 40, such as air guns and/or the like. In oneimplementation, seismic sources 40 may be coupled to, or towed by, thesurvey vessel 20. In another implementation, seismic sources 40 mayoperate independently of the survey vessel 20 in that the sources 40 maybe coupled to other vessels or buoys.

A particular seismic source 40 may be part of an array of seismic sourceelements (such as air guns, for example) that may be arranged in strings(gun strings, for example) of the array. Regardless of the particularcomposition of the seismic sources, the sources may be fired in aparticular time sequence during the survey.

As seismic streamers 30 are towed behind the survey vessel 20, acousticsignals 42, which may be referred to as “shots,” may be produced byseismic sources 40 and are directed down through a water column 44 intostrata 62 and 68 beneath a water bottom surface 24. Acoustic signals 42may be reflected from the various subterranean geological formations,such as formation 65 depicted in FIG. 1 .

The incident acoustic signals 42 that are generated by the sources 40may produce corresponding reflected acoustic signals, or pressure waves60, which may be sensed by seismic sensor units 58. In oneimplementation, pressure waves received and sensed by seismic sensorunits 58 may include “up going” pressure waves that propagate to thesensor units 58 without reflection, as well as “down going” pressurewaves that are produced by reflections of the pressure waves 60 fromair-water boundary 31.

Seismic sensor units 58 may generate signals, called “traces,” whichindicate the acquired measurements of the pressure wavefield and/orparticle motion. The traces (i.e., seismic data) may be recorded and maybe processed by signal processing unit 23 deployed on the survey vessel20.

The seismic data may be used to build up an image of a survey area forpurposes of identifying subterranean geological formations, such as thegeological formation 65. Subsequent analysis of the image may revealprobable locations of hydrocarbon deposits in subterranean geologicalformations. Analysis of the image may also be used for other purposes,such as Carbon Capture and Sequestration (CCS), geotechnicalapplications, and the like. In one implementation, portions of theanalysis of the image may be performed on the seismic survey vessel 20,such as by the signal processing unit 23.

Although FIG. 1 illustrates a marine-based seismic acquisition system,the marine-based seismic acquisition system is provided as an example ofa seismic acquisition system that may correspond to the implementationsdescribed herein. However, it should be noted that the implementationsdescribed herein may also be performed on other seismic acquisitionsystems.

Seismic Data Processing

As described above, seismic data, such as pressure data and/or particlemotion data, may be acquired using a marine-based seismic acquisitionsystem. In some scenarios, increasing the number of seismic streamersemployed in such acquisition systems may lead to an increase in seismicdata, and thereby an increase in the accuracy of images constructed fora survey area. However, the number of seismic streamers employed in suchacquisition systems may be limited. In particular, the number ofstreamers that can be towed by a survey vessel may be limited due toissues relating to drag, entanglement of streamers, power constraints ofthe survey vessel, and/or the like.

In such scenarios, interpolation of the seismic data may be used. Inparticular, the interpolation may be used to construct seismic databetween seismic streamers, such as where streamers are not present inthe seismic acquisition system. In such scenarios, however, theinterpolation and a subsequent noise attenuation of the seismic data maybe compromised due to the existence of noise in the seismic data. Suchnoise may arise due to the sensitivity of the seismic sensors usedduring acquisition. In other scenarios, the interpolation and subsequentnoise attenuation of the seismic data may be compromised due to theseismic data having been acquired via bad sensors. In particular, dataacquired via the bad sensors may get smeared during noise attenuationand may degrade the quality of the seismic data acquired via goodsensors.

As further described below, in some implementations, a quality control(QC) process, a preconditioning process, and a noise attenuation processmay be performed on the seismic data. Such processing may be used toaccount for noise in the seismic data, as well as seismic data that mayhave been acquired via bad sensors.

In particular, and as further described below, the quality controlprocess may be used to identify bad traces of the acquired seismic data.The bad traces may be composed of seismic data obtained from one or morebad sensors, such as dead, weak, noisy, and/or spiky sensors. Thequality control process may not be used to substantively change valuesof the bad traces, except for traces which include digital spikes, asfurther described below.

The preconditioning process may be used to perform one or morecorrective actions on the QC processed seismic data, as furtherdescribed below. Further, the noise attenuation process may be used toremove noise from the preconditioning processed seismic data, where thenoise attenuated seismic data may be used for later seismic processing.

In a further implementation, the QC process, the preconditioningprocess, and the noise attenuation process may be performed in real timeor substantially near real time. In particular, the processes may beperformed for seismic data corresponding to a particular shot, such thatthe processes may be completed before seismic data corresponding to asubsequent shot is received.

As mentioned above, the seismic data may include particle motion dataand pressure data. In one implementation, the multi-component seismicsensor unit used to acquire the particle motion data and the pressuredata may be composed of two hydrophones (as described above) incombination with three-component or two-component accelerometers (asdescribed above).

The QC process, the preconditioning process, and the noise attenuationprocess applied to the particle motion data may be different than thoseapplied to the pressure data. Further, the processes for the particlemotion data and the processes for the hydrophone data may be performedin parallel.

Particle Motion Data Processing

FIG. 2 illustrates a flow diagram of a method 200 for performing a QCprocess on particle motion data in accordance with implementations ofvarious techniques described herein. In one implementation, method 200may be performed by a computer application. It should be understood thatwhile method 200 indicates a particular order of execution ofoperations, in some implementations, certain portions of the operationsmight be executed in a different order. Further, in someimplementations, additional operations or blocks may be added to themethod. Likewise, some operations or blocks may be omitted.

At block 210, particle motion data may be received. As mentioned above,the particle motion data may be received from a plurality of seismicsensor units disposed on a plurality of seismic streamers, where each ofthe seismic sensor units may include one or more particle motionsensors. In one implementation, each of the seismic sensor units mayinclude three-component or two-component accelerometers (as describedabove). In another implementation, one group of accelerometer componentsmay be separated from another group of accelerometer components alongeach streamer using 0.625 meter (m) spacing.

At block 220, one or more digital spikes in one or more traces of theparticle motion data may be identified. A digital spike may be a spikewhich occurs in a trace after an in-sea anti-aliasing filter is appliedto the trace. In another implementation, an amplitude based spikedetection algorithm may be used to detect such digital spikes in thefiltered traces. Other similar algorithms known to those skilled in theart may also be used.

For example, the algorithm may compute a mean or a median of the valuesof a trace of particle motion data. The algorithm may perform adifference operation on the values of the trace with the computed meanor median to identify values which differ from the mean or median by athreshold amount. Those values which differ beyond a threshold amountmay be identified as digital spikes.

Upon identification, the digital spikes of a trace may be interpolatedsuch that they may be removed from the trace. In another implementation,if the number of digital spikes in a trace exceeds a predeterminedamount, or if the digital spikes occur in more than a predeterminedamount of consecutive time samples for the trace, then the entire tracemay be marked when stored. The trace and its associated particle motionsensor may be marked as bad due to the trace being spiky. Such a markingmay allow a later seismic process to skip over the bad trace. In yetanother implementation, the digital spikes of the trace may be relatedto a transmission error from the particle motion sensor.

At block 230, calibrations of one or more particle motion sensors may bedetermined. As mentioned above, the particle motion sensors may includethe three-component accelerometers or any other implementation known tothose skilled in the art.

In particular, the direct current (DC) offset values of the particlemotion sensors may be used to compute the orientation of the particlemotion sensors. The DC offset value may be the same as the amplitude ofa signal having a frequency of 0 hertz (Hz). Any errors in the DC valuesmay limit the ability to process the particle motion data, such as byrotating the data, as further described below. In some scenarios, lowfrequency noise due to rip currents or swell may interfere with thegravity measurements of the particle motion sensors.

In one implementation, the calibration of the particle motion sensorsmay be found by computing a median of the length of a gravity vectorcorresponding to the particle motion sensors. The median may then becompared against the standard gravity value of 9.81 m/(second (s))².

In particular, an expected value of a noisy gravity measurement may beestimated for a particle motion sensor by running a median filter oncomputed gravity values. The gravity measurement from each particlemotion sensor may then be compared with the expected value of the noisygravity measurement. The particle motion sensors having deviationsgreater than a threshold amount may be marked as uncalibrated. Inparticular, the particle motion sensor and/or its associated traces maybe marked as bad due to the traces and/or sensor being uncalibrated.Such a marking may allow a later seismic process to skip over the badtraces.

At block 240, orientation angle errors of the particle motion sensorsmay be determined based on low frequency noise of the particle motiondata. In one scenario, to acquire a high quality accelerationmeasurement from, for example, a three-component accelerometer, thesensor orientation angle of the three-component accelerometer should bemeasured with accuracy. In particular, in the presence of strong lowfrequency noise, a DC measurement using the accelerometer may beincorrect, and the accuracy of the computed orientation angle for theaccelerometer may be compromised.

Accordingly, in one implementation, the amount of orientation angleerrors may be determined based on low frequency noise amplitude. In afurther implementation, to find the orientation angle errors, the lowfrequency noise amplitude may be estimated based on the particle motiondata. The low frequency noise amplitude may be estimated from theparticle motion data using any implementation known to those skilled inthe art.

In one example, for low frequency (e.g., less than 5 Hz) noiseamplitudes of particle acceleration data having values less than 85decibels (dB), the orientation angle error for an accelerometer may bedetermined to be less than 2 degrees. Such an amount of orientationangle error may indicate accuracy in the orientation angle. Accordingly,the accelerometer and its associated data may be flagged or marked asaccurate for use in later seismic processing.

In another example, for low frequency (e.g., less than 5 Hz) noiseamplitudes of particle acceleration data having values between 85-95 dB,the orientation angle error for an accelerometer may be determined to bebetween 2-7 degrees. Such an amount of orientation angle error may beconsidered less than ideal, but acceptable. Accordingly, theaccelerometer and its associated data may be flagged or marked asacceptable for use in later seismic processing.

In another example, for low frequency (e.g., less than 5 Hz) noiseamplitudes of particle acceleration data having values greater than 95dB, the orientation angle error for an accelerometer may be determinedto be greater than 7 degrees. Such an amount of orientation angle errormay be considered to be unacceptable. Accordingly, the accelerometer andits associated data may be flagged or marked as unacceptable for use inlater seismic processing.

At block 250, an amount of cable twists for each of the seismicstreamers may be determined. A streamer towed behind a seismic vesselmay experience twists along its length. The twist may be caused by therotational imbalances, by active steering devices, or if some debris gettrapped to the streamer. The twist may have no effect on the scalarpressure wavefield, and it may have no negative effect on the acquiredparticle motion data as long as the sensor orientation is accuratelycomputed.

Monitoring the twist in the streamer may be used to ensure themechanical health of the streamer. Although streamers can tolerate alarge number of twists along their length, if the number of twistsbecomes excessive there could be physical damage to the streamer or thewires within the streamer.

In one implementation, the particle motion sensors, such asaccelerometers, disposed on a streamer may be used to measure the numberof twists in the streamer. In particular, the streamer may be dividedinto portions along its length, with each portion having a set ofparticle motion sensors. The orientation of each portion of the streameris measured using its set of sensors over time and then compared. If thecomparison indicates that the orientation of a portion has changedbeyond a predetermined threshold, then a cable twist for that portionmay have occurred. If the number of cable twists for the portions of thestreamer exceeds a set number, then a notification may be sent to thevessel which indicates that the streamer has exceeded the set number ofcable twists.

At block 260, a validity of the one or more of the particle motionsensors may be determined based on at least one power spectral density(PSD) threshold curve. A particle motion sensor may be invalid if it isconsidered to be dead, weak, or noisy. A dead sensor may be a sensorwhich does not transmit data. A weak sensor may be a sensor which haslost sensitivity to signal. A noisy sensor may be a sensor with highself-noise (e.g., electronic noise) or environmental noise.

In one implementation, PSD may describe how the power of a signal may bedistributed over frequency. In another implementation, the at least onePSD threshold curve may be generated based on any method known to thoseskilled in the art. For example, in one such implementation, asdisclosed in commonly assigned U.S. Pat. No. 8,639,442, the entirety ofwhich is herein incorporated by reference, the one or more particlemotion sensors may be determined to be invalid based on upper and lowerthreshold curves.

In particular, in such an implementation, particle motion data may bereceived from a first particle motion sensor in a seismic survey. A PSDcurve for the particle motion data may then be generated. In a furtherimplementation, the PSD curve may be generated using predeterminedfrequency bands. A maximum PSD value of the PSD curve may then begenerated. The above steps may be repeated for particle motion datareceived from other particle motion sensors in the survey. Further, anexpected maximum PSD curve may be generated for each particle motionsensor using a smoothing filter. An upper and lower threshold curves maythen be generated based on the expected maximum PSD curve. The maximumPSD value corresponding to each particle motion sensor may be comparedto the upper and lower threshold curves to determine whether particlemotion data acquired by each particle motion sensor is noisy ordead/weak. In a further implementation, if a maximum PSD value isgreater than the upper threshold curve, then the particle motion sensorthat corresponds to that maximum PSD value may be characterized asnoisy. In addition, if a maximum PSD value is less than the lowerthreshold curve, then the particle motion data that corresponds to thatmaximum PSD value may be characterized as dead or weak.

In another implementation, the frequency band of 170-190 Hz may be usedto detect particle motion sensors with high self noise, and thefrequency band of 70-90 Hz may be used to detect particle motion sensorswith high noise levels in the seismic frequency band.

After identifying the invalid particle motion sensors, the particlemotion data that corresponds to the identified sensors may be muted, andnew particle motion data may be interpolated to replace the mutedparticle motion data. The new particle motion data may be interpolatedbased on the particle motion data acquired by valid particle motionsensors that are near or adjacent to the invalid particle motionsensors. In this manner, the invalid particle motion data in theparticle motion data may be corrected.

At block 270, one or more analog spikes in one or more traces of theparticle motion data may be identified. In one implementation, an analogspike may occur before the application of the in-sea anti-aliasingfilter to the traces. In another implementation, an energy based spikedetection algorithm may be used to detect such analog spikes in thetraces, including such algorithms known to those skilled in the art.

The analog spike may affect large blocks of a trace, such that theanalog spikes may be smeared across multiple blocks. Accordingly, theenergy based spike detection algorithm may compute the energy of a tracein overlapping blocks. In a further implementation, the algorithm maycompute a mean or a median of the values of each block of a trace ofparticle motion data. The algorithm may perform a difference operationon the values of the block with the computed mean or median to identifyvalues which differ from the mean or median by a threshold amount. Thosevalues which differ beyond a threshold amount may be identified asanalog spikes. In another implementation, the analog spikes may bedetermined for each component (e.g., inline, crossline, vertical) of theparticle motion data for the particle motion sensor.

Upon identification, the analog spikes of a trace may be interpolatedsuch that they may be removed from the block. In another implementation,if the number of analog spikes in a trace exceeds a predeterminedamount, then the entire trace may be marked when stored. The trace andits associated particle motion sensor may be marked as bad due to thetrace and sensor being spiky. Such a marking may allow a later seismicprocess to skip over the bad trace. In another implementation, thecrossline and vertical values of the particle motion data may be rotatedprior to application of the algorithm to avoid variations in signalenergy due to cable twist of the streamers.

At block 280, one or more analog spikes not identified at block 270 maybe identified based on an estimated gravity vector for each particlemotion sensor. As mentioned above, the particle motion sensors mayinclude the three-component accelerometers or any other implementationknown to those skilled in the art.

An energy based spike detection algorithm similar to that used at block270 may detect spikes in the DC value of a particle motion sensor bychecking whether the estimated gravity vector for the sensor deviatespositively or negatively from the standard gravity value of 9.81m/(second (s))². In a further implementation, the algorithm maycontinuously check for such deviations. The deviations may indicate thepresence of analog spikes.

As similarly discussed above, the analog spikes may affect large blocksof a trace for a particle motion sensor, such that the analog spikes maybe smeared across multiple blocks. Accordingly, the analog spikes of atrace may be located in overlapping blocks. The deviations discussedabove may be used to create pointers for identifying the blockscontaining the spikes.

Upon identification, the analog spikes of a trace may be interpolatedsuch that they may be removed from the block. In another implementation,if the number of analog spikes in a trace exceeds a predeterminedamount, then the entire trace may be marked when stored. The trace andits associated particle motion sensor may be marked as bad due to thetrace and sensor being spiky. Such a marking may allow a later seismicprocess to skip over the bad trace.

After performing the QC process on the acquired particle motion data, asdescribed above with respect to FIG. 2 , the preconditioning process maybe applied to the QC-processed particle motion data. In oneimplementation, the preconditioning process may be applied to thecrossline data and the vertical data of the QC-processed particle motiondata.

FIG. 3 illustrates a flow diagram of a method 300 for performing apreconditioning process on QC-processed particle motion data inaccordance with implementations of various techniques described herein.In one implementation, method 300 may be performed by a computerapplication. It should be understood that while method 300 indicates aparticular order of execution of operations, in some implementations,certain portions of the operations might be executed in a differentorder. Further, in some implementations, additional operations or blocksmay be added to the method. Likewise, some operations or blocks may beomitted.

At block 310, the QC-processed particle motion data may be received. Asmentioned above, the crossline data and the vertical data of theQC-processed particle motion data may be received.

At block 320, one or more dummy traces can be generated to account forthe inline gaps between consecutive sections due to connectors for theQC-processed particle motion data. In one implementation, the dummytraces may be generated for a shot record. A shot record may containparticle motion data from each particle motion sensor for one or moreshots, with each sensor represented in the shot record by a trace.

In some implementations, the shot record may include one or more dummytraces to represent one or more inserts positioned between the sensorsalong the streamer. The inserts may include birds, fins, power supply,acoustic positioning devices and transmitter, and/or the like. The dummytraces may contain no data and may be used to account for the gaps inthe streamers.

In one implementation, the generating of dummy traces, which we willrefer to as trace inserts, may be used in the shot record in order toreduce an amount of irregularity. The amount of trace inserts needed maybe proportional to the length of the inserts disposed in the streamer.Further, the trace inserts may be generated prior to a torsionalvibration noise based rotation (as described below), as such a rotationmay not be applied when there are gaps in the shot record. Oncegenerated and inserted into the shot record for the gaps, these traceinserts may be marked as dead traces, and may be interpolated laterduring seismic processing.

At block 330, a restoration of one or more analog spikes may beperformed on the QC-processed particle motion data. In oneimplementation, analog spikes previously marked or flagged as part of atrace or block of traces may be restored by performing interpolation onthe analog spikes. In a further implementation, the restoration (i.e.,interpolation) may be performed on spikes in a trace or block of tracesif the number of spikes in the trace or block of traces is lower than apredetermined threshold. In contrast, if the number of spikes in thetrace or block of traces is higher than the predetermined threshold,then the entire trace or block of traces may be interpolated. Othermethods of performing a restoration of analog spikes known to thoseskilled in the art may be used.

At block 340, torsional vibrational noise in the particle motion datamay be estimated, and the particle motion data may be rotated in orderto adjust the particle motion data. As a seismic streamer is towed, thestreamer may rotate, which can cause the particle motion sensors of thestreamer to rotate away from a reference coordinate system. If therotation of the streamer is not accounted for, the results obtained fromprocessing the particle motion data may not be accurate.

In particular, using implementations known to those skilled in the art,torsional vibrational noise in the particle motion data may beestimated, and the particle motion data may be rotated in order toadjust the particle motion data based on the torsional vibrationalnoise.

In one implementation, methods and systems disclosed in commonlyassigned U.S. patent application Ser. No. 13/194,512 entitledDETERMINING AN ORIENTATION ANGLE OF A SURVEY SENSOR, incorporated hereinby reference in its entirety, may be used. In such an implementation, afirst orientation angle component of the orientation angle may bedetermined, based on estimating torsional vibrational noise in theparticle motion data. Further, a first orientation angle component of anorientation angle relating to angular rotation of a particle motionsensor with respect to a reference coordinate system may be determined.The first orientation angle component may vary at a higher rate than asecond orientation angle component (i.e., a DC orientation angle, asfurther described below). The orientation angle may be provided byaggregating the first and second orientation angle components. Theparticle motion data may then be corrected by rotating the particlemotion data based on the orientation angle.

At block 350, DC orientation angle in the particle motion data may beestimated, and the particle motion data may be rotated in order toadjust the particle motion data based on the DC orientation angle. Inone implementation, the particle motion sensors may measure both thecosine and the sine of an orientation angle and an angular acceleration.These two different quantities may allow for measurement of dynamicalorientation angle changes with frequency content up to about 20 Hz. Analgorithm may compute the dynamical orientation angle with frequencycontent having a range between about 0.25 Hz to about 20 Hz. Thealgorithm may initially separate the angular acceleration from thevibration noise and signal. The algorithm may then integrate the angularacceleration twice to get the estimate of the angle at frequencies aboveabout 0.25 Hz. This component of the orientation angle may be referredto as the dynamical orientation angle. After computing the dynamicalorientation angle, the particle motion data may rotated. The frequencycontent of the remaining component of the orientation angle may be0-0.25 Hz. The partially rotated particle motion data may contain thesine and the cosine of this orientation angle with a frequency content0-0.5 Hz. The algorithm may separate this low-frequency sine and thecosine measurement from other noise and signal terms. The arctangent ofthe computed sine and the cosine signals may provide the secondcomponent of the orientation angle. In another implementation, the DCorientation angle may be less than 1 Hz.

The total orientation angle may be obtained by summing the twocomponents of the orientation angle. After the second rotation, thevertical data may be aligned with the vertical direction (pointingdownwards), and the crossline data may be aligned with the crosslinedirection.

At block 360, a digital low cut filter may be applied to theQC-processed particle motion data. Any digital low cut filter known tothose skilled in the art may be used. In one implementation, one or morethird order Butterworth filters may be used, such as with 1.5 Hz and 3Hz corner frequencies respectively. The filters may be applied in bothforwards and reverse directions, thereby making the filters sixth orderand avoiding phase distortion. Therefore, the effective roll-rate of thefilters may be 36 dB/octave. The low frequencies of accelerometers maybe discarded.

In another implementation, guard window tapering may be applied inconjunction with the filters to reduce filtering edge effects. The taperlength of the guard window tapering may be chosen to be comparable to orless than the length of the impulse response of the digital low-cutfilter.

At block 370, a trace interpolation may be performed on the QC-processedparticle motion data. In one implementation, the trace interpolation maybe performed on marked or flagged bad traces, such as those discussedabove. Further, the trace interpolation may be performed on the traceinserts discussed above. Any trace interpolation process known to thoseskilled in the art may be used.

At block 380, an inline regularization may be performed on theinterpolated particle motion data. Due to the inserts (e.g., birds,fins, etc.) mentioned above and other components (e.g., connectors)disposed on the seismic streamers, the spacing between the last particlemotion sensor of one section of a shot record and the first particlemotion sensor of the next section of the shot record of the particlemotion data may not correspond to the actual particle motion sensorspacing. Any inline regularization method known to those skilled in theart may be used to regularize the spacing of the traces of the particlemotion data.

After performing the preconditioning process on the QC-processedparticle motion data, a noise attenuation may be performed on the inlineregularized particle motion data. Any noise attenuation method known tothose skilled in the art may be used. In one implementation, methods andsystems disclosed in commonly assigned U.S. Pat. No. 8,773,949 entitledREMOVING NOISE FROM A SEISMIC MEASUREMENT, incorporated herein byreference in its entirety, may be used. For example, the noiseattenuation may be a multi-scale noise attenuation.

The noise attenuated particle motion data may, along with noiseattenuated pressure data (as described below), include a crosslinecomponent and a vertical component, where the traces of the data may beuniformly spaced. Such data may be used to build up an image of a surveyarea for purposes of identifying subterranean geological formations,such as the geological formation. Subsequent analysis of the image mayreveal probable locations of hydrocarbon deposits in subterraneangeological formations.

Pressure Data Processing

FIG. 4 illustrates a flow diagram of a method 400 for performing a QCprocess on pressure data in accordance with implementations of varioustechniques described herein. In one implementation, method 400 may beperformed by a computer application. It should be understood that whilemethod 400 indicates a particular order of execution of operations, insome implementations, certain portions of the operations might beexecuted in a different order. Further, in some implementations,additional operations or blocks may be added to the method. Likewise,some operations or blocks may be omitted.

At block 410, pressure data may be received. As mentioned above, thepressure data may be received from a plurality of seismic sensor unitsdisposed on a plurality of seismic streamers, where each of the seismicsensor units may include one or more pressure sensors. In oneimplementation, the seismic sensor units may each include twohydrophones (as described above). In another implementation, thehydrophone pairs may be separated from other hydrophone pairs along astreamer using 3.125 m spacing.

At block 420, one or more digital spikes in one or more traces of thepressure data may be identified. The digital spikes may be similar tothose discussed above with respect to FIG. 2 . Further, a similaramplitude based spike detection algorithm discussed with respect to FIG.2 may be used to detect such digital spikes.

Upon identification, the digital spikes of a trace may be interpolatedsuch that they may be removed from the trace. In another implementation,if the number of digital spikes in a trace exceeds a predeterminedamount, or if the digital spikes occur in more than a predeterminedamount of consecutive time samples for the trace, then the entire tracemay be marked when stored. The trace and its associated pressure sensormay be marked as bad due to the trace and sensor being spiky. Such amarking may allow a later seismic process to skip over the bad trace. Inyet another implementation, the digital spikes of the trace may berelated to a transmission error from the pressure sensor.

At block 430, a correlation of the pressure data may be performed. Inparticular, a cross correlation coefficient may be calculated for theindividual hydrophones of each hydrophone pair. Any implementation knownin the art for calculating the cross correlation coefficient may beused. In one implementation, the coefficient of the pressure datareceived from each individual hydrophone of the hydrophone pair may becomputed. If the coefficient indicates that the pressure data receivedfrom each hydrophone are different, then one of the individualhydrophones of the pair may be corrupted.

In one implementation, the coefficient may range in value between a −1to a +1. For instance, if one of the individual hydrophones hasincorrect polarity, then the correlation coefficient may be equal to a−1 value. In another example, if one of the individual hydrophonesdiffers from the other by an amount between −1 and +1, then one of theindividual hydrophones may measure pressure data differently.

At block 440, a validity of one or more of the pressure sensors may bedetermined based on at least one PSD threshold curve.

For pressure sensors, the at least one PSD threshold curve may usespectra with two different parameter sets. The first set of parametersmay be used to identify noisy traces at 5-30 Hz and may be compatiblewith a root mean square (RMS) technique for finding invalid pressuresensors. The second set of parameters may be used to identify weaktraces and their corresponding weak pressure sensors which have lostsensitivity to signal on an automatically computed signal window. Theprocess for determining the validity of the pressure sensors based on aPSD threshold curve may be performed on summed hydrophones as well as toidentify collocated pressure sensors that have opposite polarity.

Further, as similar discussed above with respect to FIG. 2 , comparingthe metrics for different pressure sensors using PSD threshold curvesmay be used to identify the outliers. For instance, pressure sensorswith very high amplitude spectrum may be labeled as noisy, and pressuresensors with very low amplitude spectrum may be labeled as weak (smallvalue) or dead (zero value).

In some implementation, the determination of validity may be performedthree times for the pressure sensors. During the first run, the validityof single hydrophones (before summation) may be determined on anautomatically selected signal window to estimate the relativesensitivity of each pressure sensor. During the second run, a temporarysensitivity correction may be applied based on the result of the firstrun. After the second run, a temporary hydrophone summation may beperformed. During the third run, a validity of summed hydrophones (aftersensitivity correction) may be determined on an automatically selectedsignal window to identify additional noisy and weak sensors. Weaksensors after summation that are identified by the third PSD QC arelikely to be caused by polarity errors.

After identifying the invalid pressure sensors, the pressure data thatcorresponds to the identified sensors may be muted, and new pressuredata may be interpolated to replace the muted pressure data. The newpressure data may be interpolated based on the pressure data acquired byvalid pressure sensors that are near or adjacent to the invalid pressuresensors. In this manner, the invalid pressure data in the particlemotion data may be corrected.

At block 450, one or more analog spikes in one or more traces of thepressure data may be identified. The analog spikes may be similar to theanalog spikes discussed above with respect to FIG. 2 . Further, analgorithm similar to that discussed above with respect to FIG. 2 may beused to detect the analog spikes in the pressure data.

Upon identification, the analog spikes of a trace may be interpolatedsuch that they may be removed from the block. In another implementation,if the number of analog spikes in a trace exceeds a predeterminedamount, then the entire trace may be marked when stored. The trace andits associated pressure sensor may be marked as bad due to the trace andsensor being spiky. Such a marking may allow a later seismic process toskip over the bad trace.

At block 460, a DC offset for each trace of the pressure data may bedetermined. Traces of pressure data with relatively large DC offsets maymeasure as a distorted signal. Accordingly, an algorithm may be used toidentify and flag traces of the pressure data having a DC offset largerthan a threshold amount. Such flagged or marked traces may be muted andinterpolated during later processing. Any algorithm known to thoseskilled in the art may be used.

At block 470, earth leakage for the pressure data may be determined. Inone implementation, earth leakage may occur in the event of a shortcircuit in a streamer. In such an event, then the DC offsets of thepressure sensors of the streamer may change suddenly during a period oftime with the earth leakage. The change in DC offsets may have an impacton the pressure data.

An algorithm may be used which detects the earth leakage and provides anotification to the survey vessel of the existence of the earth leakage.In one implementation, the algorithm may measure the DC offsets of eachpressure sensor in a streamer over time. In the event of a sudden changein the DC offsets, the previous offsets may be compared to the newoffsets. Based on the comparison, an earth leakage may be detected and anotification may be sent to the survey vessel. In anotherimplementation, the earth leakage may be detected using a computedsmash-stack of traces of the pressure data, or any other implementationknown to those skilled in the art.

After performing the QC process on the acquired pressure data, asdescribed above with respect to FIG. 2 , a preconditioning process maybe applied to the QC-processed pressure data.

FIG. 5 illustrates a flow diagram of a method 500 for performing apreconditioning process on QC-processed pressure data in accordance withimplementations of various techniques described herein. In oneimplementation, method 500 may be performed by a computer application.It should be understood that while method 500 indicates a particularorder of execution of operations, in some implementations, certainportions of the operations might be executed in a different order.Further, in some implementations, additional operations or blocks may beadded to the method. Likewise, some operations or blocks may be omitted.

At block 510, the QC-processed pressure data may be received. In oneimplementation, pressure data from each individual hydrophone of ahydrophone pair may be received.

At block 520, as mentioned above with respect to FIG. 4 , a sensitivitycorrection of the hydrophones may be performed. In particular, weakhydrophones may have scalar correction factors applied to their weakpressure data. After application of the scalar correction factors, thepressure data may fall within a predetermined threshold such that it isno longer considered weak.

At block 530, pressure data from individual hydrophones of a hydrophonepair may be summed. In one implementation, given that the individualhydrophones may be mounted at opposite sides of a central axis of astreamer, the respective pressure data received at the individualhydrophones may have the same polarity. Further, given such positioning,the respective transversal vibration noise of the pressure data receivedat the individual hydrophones may have an opposite polarity.

Accordingly, by summing their respective pressure data, transversalvibration noise received by the individual hydrophones may beattenuated. After hydrophone summation, the number of hydrophones in astreamer may be halved. After summing the pressure data, the data may bedivided by two to find the average. In another implementation, if one ofthe individual hydrophones is flagged as a bad sensor, then the otherindividual hydrophone may be used without performing the summation.

At block 540, one or more trace inserts may be generated for theQC-processed pressure data. The trace inserts may be generated assimilarly discussed above with respect to FIG. 3 .

At block 550, a digital low cut filter may be applied to theQC-processed pressure data. The digital low cut filter may be applied assimilarly discussed above with respect to FIG. 3 . In anotherimplementation, guard window tapering may be applied in conjunction withthe filters to reduce filtering edge effects. The taper length of theguard window tapering may be chosen to be comparable to or less than thelength of the impulse response of the digital low-cut filter.

At block 560, a trace interpolation may be performed on the QC-processedpressure data. In one implementation, the trace interpolation may beperformed on marked or flagged bad traces, such as those discussedabove. Further, the trace interpolation may be performed on the traceinserts discussed above. Any trace interpolation process known to thoseskilled in the art may be used.

At block 570, an inline regularization may be performed on theinterpolated pressure data. Due to the inserts (e.g., birds, fins, etc.)mentioned above and other components (e.g., connectors) disposed on theseismic streamers, the spacing between the last pressure sensor of onesection of a shot record and the first pressure sensor of the nextsection of the shot record of the pressure data may not correspond tothe actual pressure sensor spacing. Any inline regularization methodknown to those skilled in the art may be used to regularize the spacingof the traces of the pressure data.

After performing the preconditioning process on the QC-processedpressure, a noise attenuation may be performed on the inline regularizedparticle motion data. Any noise attenuation method known to thoseskilled in the art may be used, as similarly discussed above withrespect to particle motion data. The noise attenuation pressure data maybe used to build up an image of a survey area for purposes ofidentifying subterranean geological formations, such as the geologicalformation. Subsequent analysis of the image may reveal probablelocations of hydrocarbon deposits in subterranean geological formations.

Computing Systems

Implementations of various technologies described herein may beoperational with numerous general purpose or special purpose computingsystem environments or configurations. Examples of well known computingsystems, environments, and/or configurations that may be suitable foruse with the various technologies described herein include, but are notlimited to, personal computers, server computers, hand-held or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputers,mainframe computers, smartphones, smartwatches, personal wearablecomputing systems networked with other computing systems, tabletcomputers, and distributed computing environments that include any ofthe above systems or devices, and the like.

The various technologies described herein may be implemented in thegeneral context of computer-executable instructions, such as programmodules, being executed by a computer. Generally, program modulesinclude routines, programs, objects, components, data structures, etc.that performs particular tasks or implement particular abstract datatypes. While program modules may execute on a single computing system,it should be appreciated that, in some implementations, program modulesmay be implemented on separate computing systems or devices adapted tocommunicate with one another. A program module may also be somecombination of hardware and software where particular tasks performed bythe program module may be done either through hardware, software, orboth.

The various technologies described herein may also be implemented indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network,e.g., by hardwired links, wireless links, or combinations thereof. Thedistributed computing environments may span multiple continents andmultiple vessels, ships or boats. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

FIG. 6 illustrates a schematic diagram of a computing system 600 inwhich the various technologies described herein may be incorporated andpracticed. Although the computing system 600 may be a conventionaldesktop or a server computer, as described above, other computer systemconfigurations may be used.

The computing system 600 may include a central processing unit (CPU)630, a system memory 626, a graphics processing unit (GPU) 631 and asystem bus 628 that couples various system components including thesystem memory 626 to the CPU 630. Although one CPU is illustrated inFIG. 6 , it should be understood that in some implementations thecomputing system 600 may include more than one CPU. The GPU 631 may be amicroprocessor specifically designed to manipulate and implementcomputer graphics. The CPU 630 may offload work to the GPU 631. The GPU631 may have its own graphics memory, and/or may have access to aportion of the system memory 626. As with the CPU 630, the GPU 631 mayinclude one or more processing units, and the processing units mayinclude one or more cores. The system bus 628 may be any of severaltypes of bus structures, including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus also known as Mezzanine bus. The system memory626 may include a read-only memory (ROM) 612 and a random access memory(RAM) 646. A basic input/output system (BIOS) 614, containing the basicroutines that help transfer information between elements within thecomputing system 600, such as during start-up, may be stored in the ROM612.

The computing system 600 may further include a hard disk drive 650 forreading from and writing to a hard disk, a magnetic disk drive 652 forreading from and writing to a removable magnetic disk 656, and anoptical disk drive 654 for reading from and writing to a removableoptical disk 658, such as a CD ROM or other optical media. The hard diskdrive 650, the magnetic disk drive 652, and the optical disk drive 654may be connected to the system bus 628 by a hard disk drive interface636, a magnetic disk drive interface 638, and an optical drive interface640, respectively. The drives and their associated computer-readablemedia may provide nonvolatile storage of computer-readable instructions,data structures, program modules and other data for the computing system600.

Although the computing system 600 is described herein as having a harddisk, a removable magnetic disk 656 and a removable optical disk 658, itshould be appreciated by those skilled in the art that the computingsystem 600 may also include other types of computer-readable media thatmay be accessed by a computer. For example, such computer-readable mediamay include computer storage media and communication media. Computerstorage media may include volatile and non-volatile, and removable andnon-removable media implemented in any method or technology for storageof information, such as computer-readable instructions, data structures,program modules or other data. Computer storage media may furtherinclude RAM, ROM, erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), flashmemory or other solid state memory technology, CD-ROM, digital versatiledisks (DVD), or other optical storage, magnetic cassettes, magnetictape, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store the desired information andwhich can be accessed by the computing system 600. Communication mediamay embody computer readable instructions, data structures, programmodules or other data in a modulated data signal, such as a carrier waveor other transport mechanism and may include any information deliverymedia. The term “modulated data signal” may mean a signal that has oneor more of its characteristics set or changed in such a manner as toencode information in the signal. By way of example, and not limitation,communication media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. The computing system 600 may alsoinclude a host adapter 633 that connects to a storage device 635 via asmall computer system interface (SCSI) bus, a Fiber Channel bus, aneSATA bus, or using any other applicable computer bus interface.Combinations of any of the above may also be included within the scopeof computer readable media.

A number of program modules may be stored on the hard disk 650, magneticdisk 656, optical disk 658, ROM 612 or RAM 616, including an operatingsystem 618, one or more application programs 620, program data 624, anda database system 648. The application programs 620 may include variousmobile applications (“apps”) and other applications configured toperform various methods and techniques described herein. The operatingsystem 618 may be any suitable operating system that may control theoperation of a networked personal or server computer, such as Windows®XP, Mac OS® X, Unix-variants (e.g., Linux® and BSD®), and the like.

A user may enter commands and information into the computing system 600through input devices such as a keyboard 662 and pointing device 660.Other input devices may include a microphone, joystick, game pad,satellite dish, scanner, or the like. These and other input devices maybe connected to the CPU 630 through a serial port interface 642 coupledto system bus 628, but may be connected by other interfaces, such as aparallel port, game port or a universal serial bus (USB). A monitor 634or other type of display device may also be connected to system bus 628via an interface, such as a video adapter 632. In addition to themonitor 634, the computing system 600 may further include otherperipheral output devices such as speakers and printers.

Further, the computing system 600 may operate in a networked environmentusing logical connections to one or more remote computers 674. Thelogical connections may be any connection that is commonplace inoffices, enterprise-wide computer networks, intranets, and the Internet,such as local area network (LAN) 676 and a wide area network (WAN) 666.The remote computers 674 may be another a computer, a server computer, arouter, a network PC, a peer device or other common network node, andmay include many of the elements describes above relative to thecomputing system 600. The remote computers 674 may also each includeapplication programs 670 similar to that of the computer actionfunction.

When using a LAN networking environment, the computing system 600 may beconnected to the local network 676 through a network interface oradapter 644. When used in a WAN networking environment, the computingsystem 600 may include a router 664, wireless router or other means forestablishing communication over a wide area network 666, such as theInternet. The router 664, which may be internal or external, may beconnected to the system bus 628 via the serial port interface 642. In anetworked environment, program modules depicted relative to thecomputing system 600, or portions thereof, may be stored in a remotememory storage device 672. It will be appreciated that the networkconnections shown are merely examples and other means of establishing acommunications link between the computers may be used.

The network interface 644 may also utilize remote access technologies(e.g., Remote Access Service (RAS), Virtual Private Networking (VPN),Secure Socket Layer (SSL), Layer 2 Tunneling (L2T), or any othersuitable protocol). These remote access technologies may be implementedin connection with the remote computers 674.

It should be understood that the various technologies described hereinmay be implemented in connection with hardware, software or acombination of both. Thus, various technologies, or certain aspects orportions thereof, may take the form of program code (i.e., instructions)embodied in tangible media, such as floppy diskettes, CD-ROMs, harddrives, or any other machine-readable storage medium wherein, when theprogram code is loaded into and executed by a machine, such as acomputer, the machine becomes an apparatus for practicing the varioustechnologies. In the case of program code execution on programmablecomputers, the computing device may include a processor, a storagemedium readable by the processor (including volatile and non-volatilememory and/or storage elements), at least one input device, and at leastone output device. One or more programs that may implement or utilizethe various technologies described herein may use an applicationprogramming interface (API), reusable controls, and the like. Suchprograms may be implemented in a high level procedural or objectoriented programming language to communicate with a computer system.However, the program(s) may be implemented in assembly or machinelanguage, if desired. In any case, the language may be a compiled orinterpreted language, and combined with hardware implementations. Also,the program code may execute entirely on a user's computing device, onthe user's computing device, as a stand-alone software package, on theuser's computer and on a remote computer or entirely on the remotecomputer or a server computer.

The system computer 600 may be located at a data center remote from thesurvey region. The system computer 600 may be in communication with thereceivers (either directly or via a recording unit, not shown), toreceive signals indicative of the reflected seismic energy. Thesesignals, after conventional formatting and other initial processing, maybe stored by the system computer 600 as digital data in the disk storagefor subsequent retrieval and processing in the manner described above.In one implementation, these signals and data may be sent to the systemcomputer 600 directly from sensors, such as geophones, hydrophones andthe like. When receiving data directly from the sensors, the systemcomputer 600 may be described as part of an in-field data processingsystem. In another implementation, the system computer 600 may processseismic data already stored in the disk storage. When processing datastored in the disk storage, the system computer 600 may be described aspart of a remote data processing center, separate from data acquisition.The system computer 600 may be configured to process data as part of thein-field data processing system, the remote data processing system or acombination thereof.

Those with skill in the art will appreciate that any of the listedarchitectures, features or standards discussed above with respect to theexample computing system 600 may be omitted for use with a computingsystem used in accordance with the various embodiments disclosed hereinbecause technology and standards continue to evolve over time.

Of course, many processing techniques for collected data, including oneor more of the techniques and methods disclosed herein, may also be usedsuccessfully with collected data types other than seismic data. Whilecertain implementations have been disclosed in the context of seismicdata collection and processing, those with skill in the art willrecognize that one or more of the methods, techniques, and computingsystems disclosed herein can be applied in many fields and contextswhere data involving structures arrayed in a three-dimensional spaceand/or subsurface region of interest may be collected and processed,e.g., medical imaging techniques such as tomography, ultrasound, MRI andthe like for human tissue; radar, sonar, and LIDAR imaging techniques;and other appropriate three-dimensional imaging problems.

While the foregoing is directed to implementations of varioustechnologies described herein, other and further implementations may bedevised without departing from the basic scope thereof. Although thesubject matter has been described in language specific to structuralfeatures and/or methodological acts, it is to be understood that thesubject matter defined in the appended claims is not limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as example forms of implementingthe claims.

What is claimed is:
 1. A method, comprising: receiving pressure datafrom a plurality of pressure sensors disposed on a plurality of seismicstreamers; performing quality control (QC) processing on the pressuredata by: identifying one or more digital spikes in the pressure data;determining a validity of the plurality of pressure sensors based on atleast one power spectral density (PSD) threshold curve; identifying oneor more analog spikes in the pressure data; determining one or more zerofrequency offsets for one or more traces of the pressure data; anddetermining an existence of earth leakage in one or more seismicstreamers; performing preconditioning processing on the QC-processedpressure data; and attenuating noise in the preconditioning-processedpressure data.
 2. The method of claim 1, wherein performing thepreconditioning processing comprises one or more of: generating one ormore trace inserts for the QC-processed pressure data; applying adigital low cut filter to the QC-processed pressure data; interpolatingone or more traces of the QC-processed pressure data; and performing aninline regularization on the interpolated pressure data.
 3. The methodof claim 1, wherein the plurality of pressure sensors comprise one ormore pairs of hydrophones.
 4. The method of claim 3, wherein performingthe QC processing comprises calculating a cross correlation coefficientfor individual hydrophones of respective pairs of hydrophones.
 5. Themethod of claim 3, wherein performing the preconditioning processingcomprises: performing sensitivity correction of the one or more pairs ofhydrophones; and summing the pressure data of individual hydrophones forrespective pairs of hydrophones.
 6. The method of claim 1, wherein theQC processing, the preconditioning processing, and the noise attenuationare performed in real time or substantially near real time.
 7. A method,comprising: receiving pressure data from a plurality of pressure sensorsdisposed on a plurality of seismic streamers, wherein the plurality ofpressure sensors comprise one or more pairs of hydrophones; performingquality control (QC) processing on the pressure data; performingpreconditioning processing on the QC-processed pressure data, whereinperforming the preconditioning processing comprises: performingsensitivity correction of the one or more pairs of hydrophones; andsumming the pressure data of individual hydrophones for respective pairsof hydrophones; and attenuating noise in the preconditioning-processedpressure data.
 8. The method of claim 7, wherein performing the QCprocessing comprises one or more of: identifying one or more digitalspikes in the pressure data; determining a validity of the plurality ofpressure sensors based on at least one power spectral density (PSD)threshold curve; identifying one or more analog spikes in the pressuredata; determining one or more zero frequency offsets for one or moretraces of the pressure data; and determining an existence of earthleakage in one or more seismic streamers.
 9. The method of claim 7,wherein performing the preconditioning processing comprises one or moreof: generating one or more trace inserts for the QC-processed pressuredata; applying a digital low cut filter to the QC-processed pressuredata; interpolating one or more traces of the QC-processed pressuredata; and performing an inline regularization on the interpolatedpressure data.
 10. The method of claim 7, wherein performing the QCprocessing comprises calculating a cross correlation coefficient forindividual hydrophones of respective pairs of hydrophones.
 11. Themethod of claim 7, wherein the QC processing, the preconditioningprocessing, and the noise attenuation are performed in real time orsubstantially near real time.